{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "import os\n",
    "import h5py\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "beta_path_suffix = [\"\", # this is beta 0.5\n",
    "                    \"beta0\",\n",
    "                    \"beta1\",\n",
    "                    \"beta0_25\",\n",
    "                    \"beta0_75\",\n",
    "                   ][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_path = \"/analysis/ritter/projects/Methods/LRP/data/rieke-copy1/2Node_trial0/\" + beta_path_suffix\n",
    "\n",
    "hippo_file = h5py.File('/analysis/ritter/data/ADNI_HDF5/Splits_Eitel/holdout_AD_CN_2Yr15T_plus_UniqueScreening_ID_Hippocampus.h5', 'r')\n",
    "rs_per_area_LRP = dict()\n",
    "rs_per_area_GB = dict()\n",
    "cases = ['AD', 'HC', 'TP', 'TN', 'FP', 'FN']\n",
    "for case in cases:\n",
    "    with open(os.path.join(data_path, \"LRP_area_evdcs_{case}.pkl\".format(case=case)), 'rb') as file:\n",
    "            rs_per_area_LRP.update({case: pickle.load(file)})\n",
    "\n",
    "    with open(os.path.join(data_path, \"GB_area_evdcs_{case}.pkl\".format(case=case)), 'rb') as file:\n",
    "            rs_per_area_GB.update({case: pickle.load(file)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LRP:  -0.5603004848271849 .\n",
      "GB:  0.09615624758406163 .\n"
     ]
    }
   ],
   "source": [
    "# idcs are the same for both LRP and GB\n",
    "idcs = np.array([p_idx for p_idx, _ in rs_per_area_LRP['TP'][\"Hippocampus\"]])\n",
    "\n",
    "# True values\n",
    "AD_Hvals = np.array(list(hippo_file['Hippocampus']))[idcs]\n",
    "# Relevance values (LRP)\n",
    "AD_Hrels = np.array([r for _, r in rs_per_area_LRP['TP'][\"Hippocampus\"]])\n",
    "# Sensitivity values (GB)\n",
    "AD_Hsens = np.array([r for _, r in rs_per_area_GB['TP'][\"Hippocampus\"]])\n",
    "\n",
    "\n",
    "mask = ~np.isnan(AD_Hvals)\n",
    "\n",
    "actual_LRP_corrcoef = np.corrcoef(AD_Hvals[mask], AD_Hrels[mask])[1, 0]\n",
    "actual_GB_corrcoef = np.corrcoef(AD_Hvals[mask], AD_Hsens[mask])[1, 0]\n",
    "\n",
    "print(\"LRP: \", actual_LRP_corrcoef, \".\")\n",
    "print(\"GB: \", actual_GB_corrcoef, \".\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Permutation test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from copy import copy\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LRP bigger than observed:  1000 .\n",
      "GB smaller than observed:  795 .\n",
      "LRP random order mean:  -0.010661391220588571 .\n",
      "LRP random order std:  0.13238323601255558 .\n",
      "GB random order mean:  0.011859570091072004 .\n",
      "GB random order std:  0.13538990822633692 .\n"
     ]
    }
   ],
   "source": [
    "idcs = {case: np.array([p_idx for p_idx, _ in rs_per_area_LRP[case][\"Hippocampus\"]]) for case in cases}\n",
    "\n",
    "AD_Hvals_case = {case: np.array(list(hippo_file['Hippocampus']))[idcs[case]] for case in cases}\n",
    "\n",
    "AD_Hrels_case = {case: np.array([r for _, r in rs_per_area_LRP[case][\"Hippocampus\"]]) for case in cases}\n",
    "AD_Hsens_case = {case: np.array([r for _, r in rs_per_area_GB[case][\"Hippocampus\"]]) for case in cases}\n",
    "\n",
    "\n",
    "\n",
    "LRP_coeff_rand = []\n",
    "GB_coeff_rand = []\n",
    "for i in range(1000):\n",
    "    idcss = copy(idcs['TP'])\n",
    "    np.random.shuffle(idcss)\n",
    "    AD_Hvals = np.array(list(hippo_file['Hippocampus']))[idcss]\n",
    "    mask = ~np.isnan(AD_Hvals)\n",
    "\n",
    "    LRP_corrcoef = np.corrcoef(AD_Hvals[mask], AD_Hrels[mask])[1, 0]\n",
    "    GB_corrcoef = np.corrcoef(AD_Hvals[mask], AD_Hsens[mask])[1, 0]\n",
    "    \n",
    "    LRP_coeff_rand.append(LRP_corrcoef)\n",
    "    GB_coeff_rand.append(GB_corrcoef)\n",
    "    \n",
    "\n",
    "\n",
    "print(\"LRP bigger than observed: \", np.sum(actual_LRP_corrcoef < np.array(LRP_coeff_rand)), \".\")\n",
    "print(\"GB smaller than observed: \", np.sum(actual_GB_corrcoef > np.array(LRP_coeff_rand)), \".\")\n",
    "\n",
    "print(\"LRP random order mean: \", np.mean(LRP_coeff_rand), \".\")\n",
    "print(\"LRP random order std: \", np.std(LRP_coeff_rand), \".\")\n",
    "print(\"GB random order mean: \", np.mean(GB_coeff_rand), \".\")\n",
    "print(\"GB random order std: \", np.std(GB_coeff_rand), \".\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sort_idcs = np.array(sorted([(i, val) for i, val in enumerate(AD_Hvals_case['TP']) if not np.isnan(val)], key=lambda x: x[1]), dtype=int)[:, 0]\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))\n",
    "\n",
    "for case in ['TP', 'FP']:\n",
    "    ax1.scatter(AD_Hvals_case[case], AD_Hrels_case[case], label='True positives' if case == 'TP' else 'False positives')\n",
    "for case in ['TP', 'FP']:\n",
    "    ax2.scatter(AD_Hvals_case[case], AD_Hsens_case[case], label='True positives' if case == 'TP' else 'False positives')\n",
    "ax1.legend()\n",
    "ax2.legend()\n",
    "ax1.set_ylabel('LRP relevance in hippocampus')\n",
    "ax1.set_xlabel('Hippocampal volume')\n",
    "ax2.set_xlabel('Hippocampal volume')\n",
    "ax2.set_ylabel('GB susceptibility in hippocampus')\n",
    "z = np.polyfit(AD_Hvals_case['TP'][sort_idcs], AD_Hrels_case['TP'][sort_idcs], 1)\n",
    "linear = lambda x: z[1] + z[0]*x\n",
    "mini, maxi = AD_Hvals_case['TP'][sort_idcs][0], AD_Hvals_case['TP'][sort_idcs][-1]\n",
    "ax1.plot(np.linspace(mini, maxi, 2), linear(np.array([mini, maxi])))\n",
    "z = np.polyfit(AD_Hvals_case['TP'][sort_idcs], AD_Hsens_case['TP'][sort_idcs], 1)\n",
    "linear = lambda x: z[1] + z[0]*x\n",
    "mini, maxi = AD_Hvals_case['TP'][sort_idcs][0], AD_Hvals_case['TP'][sort_idcs][-1]\n",
    "ax2.plot(np.linspace(mini, maxi, 2), linear(np.array([mini, maxi])))\n",
    "\n",
    "ax1.text(4000, -0.010, 'Corr. coeff. : {:.3f}'.format(actual_LRP_corrcoef))\n",
    "ax2.text(6500, 6.5, 'Corr. coeff. : {:.3f}'.format(actual_GB_corrcoef))\n",
    "\n",
    "ax1.set_title('LRP')\n",
    "ax2.set_title('GB')\n",
    "fig.tight_layout()\n",
    "# fig.savefig(\"./correlation.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'corrcoef')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZoAAAEKCAYAAAArYJMgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJzt3XeYVeW5/vHvMzP03qSPdEFR2hbFEo1iYmyQaFCDCoogoPGky4lpJyb5oSdqTEFBg4CKgh0TS4RIMDaYQUBEERja0LvUqc/vj704biczzIaZPWv27PtzXfvaq7xrrWdR5p5VX3N3REREEiUt7AJERKRmU9CIiEhCKWhERCShFDQiIpJQChoREUkoBY2IiCSUgkZERBJKQSMiIgmloBERkYTKCLuA6qBly5beqVOnsMsQEUkq2dnZO929VXntFDRAp06dyMrKCrsMEZGkYmbr42mnU2ciIpJQChoREUkoBY2IiCSUgkZERBJKQSMiIgmloBERkYRS0IiISEIpaEREUtRDc1exYvPnCd+OHtgUEUlBTy/cwINzPyOvsIhT2zVO6LZ0RCMikmKy1u3mFy8v5/zuLfnh105J+PYUNCIiKWTrviOMfXIx7ZrW48/X9yc9zRK+TZ06ExFJEUcKirjtiSwO5xcyc/RZNKlfq0q2q6AREUkB7s7dLy5nae4+HrlhAD1aN6qybevUmYhICpj27jqeX5zLnRd359Lebap02woaEZEa7t3VO/nN3z/hklNb872Lu1f59hU0IiI12Mbdh7h95mI6t2zAA8P6kFYFF/9LUtCIiNRQh/ILGfNENoXFzpQbB9CobtVc/C9JNwOIiNRA7s5PnlvGp1s/Z+rIM+nSqmFoteiIRkSkBnrkXzn8bdkWfvz1U/jqKSeFWksoQWNmzc3sTTNbFXw3K6XNyWa22MyWmNnHZjY2mN4omHb0s9PM/hDMG2lmO2Lm3VrV+yYiEra3Vm7nvjc+5Yoz2jLugq5hlxPaqbMJwDx3n2hmE4Lxu0q02QIMcvc8M2sILDezOe6+Geh7tJGZZQMvxCw3y93vSHD9IiLVUs6OA9z59If0bNOY+645A7Oqv/hfUlinzoYA04Ph6cDQkg3cPd/d84LROpRSq5n1AE4C3k5QnSIiSWP/kQLGPJFNRpox5cYB1K9dPS7DhxU0rd19SzC8FWhdWiMz62hmy4CNwL3B0Uys64gewXjMtKvNbJmZPWdmHcsqwMzGmFmWmWXt2LGjArsiIhK+4mLn+7OWsnbnQf4yvD8dm9cPu6T/k7CgMbO5Zra8lM+Q2HZBSHhp63D3je5+BtANGGFmJQPpOuDpmPFXgE7BMm/yxVFTaeue4u4Rd4+0atXqBPZQRKT6eGjeKuZ+so2fXd6Lc7q2DLucL0nYcZW7Dy5rnpltM7O27r7FzNoC28tZ12YzWw6cDzwXrKMPkOHu2THtdsUs9hhwX0X2QUQkGby+fCsPzVvF1f07MPKcTmGX8x/COnU2BxgRDI8AXi7ZwMw6mFm9YLgZcB6wMqbJ9Xz5aIYgtI66CvikEmsWEal2Ptu2nx/OXkKfjk357Td7V4uL/yWFdaVoIjDbzEYB64FhAGYWAca6+61AL+B+M3PAgN+7+0cx6xgGXFZivXea2VVAIbAbGJnQvRARCdG+QwWMnpFFvdoZTL5hAHVrpYddUqnsy9fRU1MkEvGsrKywyxARiVtRsTPy8YW8n7OLZ8aczYCTm1d5DWaW7e6R8tpVj3vfRETkuNz3+qe8vWon/+9bp4cSMsdDr6AREUkyLy/ZxOQFOQw/K5PrB2aGXU65FDQiIklk+aZ93PX8Ms7s1IxfXnla2OXERUEjIpIkdh3I47YnsmlWvzaThg+gdkZy/AjXNRoRkSRQUFTM+KcWs/NAHs+OHUSrRnXCLiluChoRkSTwm7+t4IO1u3nw2j6c0aFp2OUcl+Q47hIRSWGzF21k+nvrufW8znyzX4ewyzluChoRkWps8YY9/Oyl5ZzXrSUTvtEz7HJOiIJGRKSa2vb5EcY+kU3rJnX40/X9yEhPzh/ZyVm1iEgNl1dYxNgns9l/pJBHb4rQrEHtsEs6YboZQESkmnF3fvHSx3y4YS+ThvenZ5vGYZdUITqiERGpZp58fz2zsjZyx1e7cdnpbctfoJpT0IiIVCMf5Ozif15ZwUU9T+IHl/QIu5xKoaAREakmNu09zPinFpPZoj5/uK4vaWnVr2+ZE6GgERGpBg7nFzFmRhb5hcU8elOExnVrhV1SpQktaMysuZm9aWargu9mx2jb2MxyzezPMdMGmNlHZrbazP5oQbdyx7NeEZHqwN2Z8MIyVmz5nD9c15eurRqGXVKlCvOIZgIwz927A/OC8bLcAywoMe1hYDTQPfhcegLrFREJ3aNv5/Dyks388JIeXNyrddjlVLowg2YIMD0Yng4MLa2RmQ0AWgP/iJnWFmjs7u97tIvQGTHLx7VeEZHqYMFnO5j42qdcdnobbv9qt7DLSYgwg6a1u28JhrcSDZMvMbM04H7gRyVmtQdyY8Zzg2lxrVdEpDpYv+sg3336Q3q0bsT/XtOH4ApAjZPQBzbNbC7QppRZd8eOuLubmZfSbjzwqrvnnshfwDHWi5mNAcYAZGZW/x7qRKRmOZhXyOgZWZjBlBsjNKhTc5+fT+ieufvgsuaZ2TYza+vuW4JTYdtLaTYION/MxgMNgdpmdgB4CIh9hWkHYFMwHM96cfcpwBSASCRSahiJiCRCcbHzw9lLWb39ADNuOYvMFvXDLimhwjx1NgcYEQyPAF4u2cDdh7t7prt3Inr6bIa7TwhOjX1uZmcHd5vdFLN8uesVEQnTn99azesfb+Wnl/XivO4twy4n4cIMmonAJWa2ChgcjGNmETN7LI7lxwOPAauBNcBrx1qviEh18OaKbTzw5md8s197Rp3XOexyqoRFb9pKbZFIxLOyssIuQ0RquNXb9zP0L+/SuWUDnh07iLq10sMuqULMLNvdI+W105sBRESqwL7DBYyekU3dWmlMvnFA0ofM8ai5tzmIiFQTRcXO9575kI27DzFz9Nm0a1ov7JKqlI5oREQS7P5/rOStlTv45VWnMbBz87DLqXIKGhGRBPrbss1Mmr+G6wd25IazUvOZPQWNiEiCrNj8OT9+dhkDTm7Gr646rcY++V8eBY2ISALsPpjPmCeyaFwvg4eH96dORupc/C9JNwOIiFSywqJi7pi5mO3785h92yBOalw37JJCpSMaEZFK9rtXP+XdNbv47dDe9O3YNOxyQqegERGpRM9n5zL1nbWMPKcT3450DLucakFBIyJSSZZu3Mt/v/gRg7q04O7Le4VdTrWhoBERqQTb9x/htieyadWwDn8Z3p9a6frxepRuBhARqaD8wmLGP7mYvYfzeX7cOTRvUDvskqoVBY2ISAX96pWPyVq/hz9d34/T2jUJu5xqR8d2IiIV8NQH65n5wQbGXtCVK/u0C7ucaklBIyJyghat282v5nzMhae04sdfPyXscqotBY2IyAnYsu8w455cTIdm9Xnoun6kp6Xm62XiEUrQmFlzM3vTzFYF382O0baxmeWa2Z+D8fpm9ncz+9TMPjaziTFtR5rZDjNbEnxurYr9EZHUcqSgiNueyOZwfiFTbhxAk3q1wi6pWgvriGYCMM/duwPzgvGy3AMsKDHt9+7eE+gHnGtm34iZN8vd+wafeLqEFhGJm7vz0xc/YlnuPh68ti/dWzcKu6RqL6ygGQJMD4anA0NLa2RmA4DWwD+OTnP3Q+7+VjCcDywGOiS0WhGRwNR31vHC4k18f3APvnZam7DLSQphBU1rd98SDG8lGiZfYmZpwP3Aj8paiZk1Ba4kelR01NVmtszMnjOzMt//YGZjzCzLzLJ27NhxQjshIqnlndU7+d2rn/C1U1vz3Yu6hV1O0khY0JjZXDNbXspnSGw7d3fAS1nFeOBVd88tY/0ZwNPAH909J5j8CtDJ3c8A3uSLo6b/4O5T3D3i7pFWrVqdwB6KSCrZuPsQt89cTNdWDXjg2r6k6eJ/3BL2wKa7Dy5rnpltM7O27r7FzNoC20tpNgg438zGAw2B2mZ2wN2PXs+ZAqxy9z/EbHNXzPKPAfdVeEdEJOUdyi9k9IwsioudKTdGaFhHz7ofj7BOnc0BRgTDI4CXSzZw9+HunununYiePptxNGTM7DdAE+B7scsEoXXUVcAnlV+6iKQSd+fHzy7js237+dN3+tOpZYOwS0o6YQXNROASM1sFDA7GMbOImR3zTjEz6wDcDZwKLC5xG/OdwS3PS4E7gZGJ2gERSQ2T5q/h7x9t4a5Le3JBD51mPxEWvUSS2iKRiGdlZYVdhohUM299up1bpi/iyjPa8dB1fTHTdZlYZpbt7pHy2unNACIipcjZcYA7n/mQU9s25t6rz1DIVICCRkSkhP1HChg9I4ta6WlMvnEA9Wqnh11SUtOtEyIiMYqLne/PWsK6XYd4ctRZdGhWP+ySkp6OaEREYvxh7mfM/WQ7v7jiVAZ1bRF2OTWCgkZEJPD68i388Z+rGRbpwE2DTg67nBpDQSMiAqzcup8fzF5K345NuWdob138r0QKGhFJeXsP5TN6RhYN62Qw+cYB1MnQxf/KpJsBRCSlFRYV892nP2TrviM8c9vZtG5cN+ySahwFjYiktPveWMnbq3Zy79Wn0z+zzD4YpQJ06kxEUtZLH25iyoIcbhp0MteemRl2OTWWgkZEUtLyTfu46/llDOzcnJ9fcWrY5dRoxwwaM+tcVYWIiFSVnQfyGDMjixYNajNpeH9qpet37kQq70/3OQAzm1dOOxGRpFBQVMz4pxaz62A+U26K0LJhnbBLqvHKuxkgzcx+CvQwsx+UnOnuDySmLBGRxLjnbytYuHY3D13Xl97tm4RdTkoo74jmOqCIaCA1KuUjIpI0Zi3awIz31jPmK10Y0rd92OWkjGMe0bj7SuBeM1vm7q9V5obNrDkwC+gErAOGufueMto2BlYAL7n7HcG0+UBb4HDQ7Gvuvt3M6gAzgAHALuBad19XmbWLSPLJXr+Hn7/0Med3b8ldl/YMu5yUEu8VsMVm9lczew3AzE41s1EV3PYEYJ67dwfmBeNluQdYUMr04e7eN/hsD6aNAva4ezfgQeDeCtYpIklu2+dHGPdkNm2a1OVP1/cjPU2vl6lK8QbNNOANoF0w/hnwvQpuewgwPRieDgwtrZGZDQBaA/84gfU+B1xsemmRSMo6UlDEbU9kcyCvkEdvitC0fu2wS0o58QZNS3efDRQDuHsh0Ws3FdHa3bcEw1uJhsmXmFkacD/wozLW8biZLTGzn8eESXtgY0yd+wC961skBbk7v3h5OUs27uWBYX04pY0uLYch3lfQHDSzFoADmNnZRH+AH5OZzQXalDLr7tgRd3cz81LajQdedffcUg5Khrv7JjNrBDwP3Ej02kxczGwMMAYgM1NPBIvURDPeW8/srFzuvKgbl/ZuG3Y5KSveoPkBMAfoambvAK2Aa8pbyN0HlzXPzLaZWVt332JmbYHtpTQbBJxvZuOBhkBtMzvg7hPcfVOwjf1mNhMYSDRoNgEdgVwzywCaEL0poGRtU4ApAJFIpLSQE5Ek9t6aXfz6bysY3Ks13xvcI+xyUlpcQePui83sAuAUwICV7l5QwW3PAUYAE4Pvl0vZ7vCjw2Y2Eoi4+4QgQJq6+04zqwVcAcwtsd73iIbhP91dQSKSQnL3HOL2mYvp1KI+D17bhzRd/A9VXEET/DAfB3wlmDTfzCZXMGwmArODu9fWA8OCbUWAse5+6zGWrQO8EdSVTjRkHg3m/RV4wsxWA7uJPgskIinicH704n9BUTGP3hShUd1aYZeU8iyeX/bN7DGgFl/czXUjUFROGCSNSCTiWVlZYZchIhXk7vzXM0t4Zdlmpo44k6/2PCnskmo0M8t290h57eK9RnOmu/eJGf+nmS09sdJERBJjyoIc5izdzI+/fopCphqJ9/bmIjPrenTEzLpQ8dubRUQqzV//vZaJr3/K5ae3ZfyFXctfQKpMvEc0PwbeMrMcojcDnAzcnLCqRETiVFzs/O7VT3js32u59LQ23D+sD3pGu3qJ966zeWbWnehdZxC96ywvcWWJiJQvr7CIH85eyt+WbWHkOZ34+RWn6vUy1VBcp87M7Hagnrsvc/dlQP3g2RYRkVDsO1TATX9dyN+WbeGnl/Xkl1cqZKqreK/RjHb3vUdHgrcsj05MSSIix7Z572G+PfldFm/Yw0PX9WXMV7rqdFk1Fu81mnQzs6MPPppZOqA304lIlftky+eMfHwhh/KKmH7LQM7p2jLskqQc8QbNG8AsM5scjN8GvJ6YkkRESvfu6p3c9kQ2Depk8Oy4QfRs0zjskiQO8QbNT4i+gHJcMP4m8FhCKhIRKcXLSzbxo2eX0rllA6bdPJB2TeuFXZLEqdygCU6TzQjeO/ZI4ksSEfmCuzN5QQ4TX/uUszo3Z8pNEZrU02tlkkm5QePuRWZ2spnVdvf8qihKRASgqNi5528rmPbuOq44oy33D+tDnYz0sMuS4xTvqbMc4B0zmwMcPDrR3R9ISFUikvKOFBTxvWeW8PrHWxl9fmf++xu99BbmJBVv0KwJPmmAuqgTkYTaczCf0TOyyN6wh19ccSq3nNc57JKkAuK9RtPI3cvqTllEpNJs3H2IEY8vJHfPYf58fX8uP0M9Yya7eK/RnFsVxYhIalu+aR83T1tEXkERT446i4Gdm4ddklSCeE+dLQmuzzzLl6/RvJCQqkQk5Sz4bAfjnsymSb1azBx3Dt1b6yx9TRHvK2jqAruAi4Arg88VJ7pRM2tuZm+a2argu9kx2jY2s1wz+3Mw3sjMlsR8dprZH4J5I81sR8y8GtExm0hN91x2LrdMW0TH5vV58fZzFTI1TLxvb67sLgEmAPPcfaKZTQjG7yqj7T3Agpha9gN9j46bWTYQe2Q1y93vqOR6RSQB3J2/vLWa3//jM87t1oJHbhigrpdroHjf3tzBzF40s+3B53kz61CB7Q7hi26hpwNDy9juAKA18I8y5vcATgLerkAtIhKCwqJi7n5pOb//x2d8s197Hh85UCFTQ8V76uxxYA7QLvi8Ekw7Ua3dfUswvJVomHyJmaUB9wPHutvtOqJHMB4z7WozW2Zmz5lZxwrUKCIJcji/iLFPZjPzgw2Mu7ArDwzrQ+2MeH8cSbKJ92aAVu4eGyzTzOx7x1rAzOYCbUqZdXfsiLu7mXkp7cYDr7p77jFe/30dcGPM+CvA0+6eZ2a3ET1auqiM+sYQfX8bmZmZx9oVEalEuw7kMWp6Fktz9/LrIadx06BOYZckCRZv0OwysxuAp4Px64neHFAmdx9c1jwz22Zmbd19i5m1BbaX0mwQcH7QwVpDoLaZHXD3CcE6+gAZ7p4ds83Ymh4D7jtGfVOAKQCRSKS0oBORSrZ+10FGTF3Iln1HeOSGAXz9tNJ+F5WaJt5j1VuAYURPc20BrgFGVmC7c4ARwfAI4OWSDdx9uLtnunsnoqfPZhwNmcD1fBF8AAShddRVwCcVqFFEKtHSjXv51qR32Xu4gJmjz1LIpJB4j2h+DYwIetbEzJoDvycaQCdiIjDbzEYB64mGGGYWAca6ezy3JQ8DLisx7U4zuwooBHZTsTAUkUry1qfbGf/UYlo0rM30WwbStVXDsEuSKmRfvo5eRiOzD929X3nTklUkEvGsrKywyxCpkWYt2sBPX1xOr7aNmDryTE5qVDfskqSSmFm2u0fKaxfvEU2amTUrcUQT77IikoLcnT/MXcVD81bxlR6tmDS8Pw3r6MdGKor3b/1+4D0zezYY/zbw28SUJCLJrqComLtf/IjZWbl8e0AHfvet06mVrtuXU1W8bwaYYWZZfHGr8LfcfUXiyhKRZHUwr5DbZy5m/sod3HlRN75/SQ+O8YiCpIC4j2ODYFG4iEiZduzP45Zpi/h48z5+983T+c5ZekZNdJ1FRCpJzo4DjHh8ITv35/PoTREu7vUfL/yQFKWgEZEKW7xhD6OmLSLNjKfHnE3fjk3DLkmqEQWNiFTImyu28d2nF9O6cV2m3zyQTi0bhF2SVDMKGhE5YU+8v55fvryc09s34a8jz6RlwzphlyTVkIJGRI6bu/O/b6xk0vw1XNzzJP70nX7Ur60fJ1I6/csQkeOSX1jMhBeW8cLiTVw/MJN7hpxGhp6RkWNQ0IhI3PYfKWD8U4t5e9VOfnhJD+64qJuekZFyKWhEJC7bPj/CzY8vYuW2/dx3zRkMi6hfQYmPgkZEyrV6+35GTF3EnkP5TB15Jhf0aBV2SZJEFDQickyL1u3m1ulZ1EpPY/Ztg+jdvknYJUmSUdCISJle+2gL/zVrCR2a1mP6LQPp2Lx+2CVJEgrtVhEza25mb5rZquC7WRntisxsSfCZEzO9s5l9YGarzWyWmdUOptcJxlcH8ztVzR6J1CyPv7OW8TMX07tdY54bd45CRk5YmPckTgDmuXt3YF4wXprD7t43+FwVM/1e4EF37wbsAUYF00cBe4LpDwbtRCROxcXO7179hP95ZQWX9GrNzNFn07xB7bDLkiQWZtAMAaYHw9OBofEuaNH7KS8Cnitl+dj1PgdcbLr/UiQueYVF/NesJUxZkMNNg07m4RsGULdWethlSZIL8xpNa3ffEgxvBcp61WvdoC+cQmCiu78EtAD2unth0CYXaB8Mtwc2Arh7oZntC9rvTMA+iNQY+w4XMPaJbN7L2cVdl/Zk7AVd9IyMVIqEBo2ZzQXalDLr7tgRd3cz8zJWc7K7bzKzLsA/zewjYF8l1DYGGAOQmak+MyS1bdl3mJFTF5Gz8wAPXtuHb/brEHZJUoMkNGjcfXBZ88xsm5m1dfctZtYW2F7GOjYF3zlmNh/oBzwPNDWzjOCopgOwKVhkE9ARyDWzDKAJsKuU9U4BpgBEIpGyQk6kxlu5dT8jH1/I/iOFTLt5IOd2axl2SVLDhHmNZg4wIhgeAbxcsoGZNTOzOsFwS+BcYIW7O/AWcE0py8eu9xrgn0F7ESnh3TU7ueaRdyl2Z/ZtgxQykhBhBs1E4BIzWwUMDsYxs4iZPRa06QVkmdlSosEyMehSGuAu4AdmtproNZi/BtP/CrQIpv+Asu9mE0lpc5ZuZuTURbRuXJcXxp/Lqe0ah12S1FCmX/ajp86ysrLCLkOkSrg7j729lt+++gkDOzXn0ZsiNKlfK+yyJAmZWba7R8prpzcDiKSQomLnN39fwePvrOPy09ty/7A+un1ZEk5BI5IijhQU8f1ZS3ht+VZuObczP7u8F2lpun1ZEk9BI5IC9h7KZ8yMbBau283PLu/Fred3CbskSSEKGpEaLnfPIUY+vogNuw7xp+v7cWWfdmGXJClGQSNSg328eR83P76IwwVFTL9lIIO6tgi7JElBChqRGurtVTsY9+RiGtXN4Plx59CjdaOwS5IUpaARqYFeWJzLT55bRreTGvL4zWfStkm9sEuSFKagEalB3J2H/7WG+15fyaAuLZh80wAa19UzMhIuBY1IDVFU7PxyznKefH8DV/Vpx/9++wzqZOgZGQmfgkakBjicX8Sdz3zImyu2cdsFXbjr6z31jIxUGwoakSS3+2A+o6YvYsnGvfzqylMZeW7nsEsS+RIFjUgS27DrECMfX0ju3sNM+k5/vnF627BLEvkPChqRJPVR7j5unraQgiJn5q1nEenUPOySREqloBFJQm+t3M7tTy2mWf3aPDNmIN1Oahh2SSJlUtCIJJnZWRv57xc+4pTWjZh285mc1Lhu2CWJHJOCRiRJuDt/nLeaB+d+xvndWzJpeH8a6RkZSQKh9LBpZs3N7E0zWxV8NyujXZGZLQk+c2KmP2VmK81suZlNNbNawfQLzWxfzDK/qKp9EkmkwqJifvriRzw49zO+1b89U0eeqZCRpBFWV84TgHnu3h2YR9ndLR92977B56qY6U8BPYHTgXrArTHz3o5Z5teJKF6kKh3KL2TME9k8vXAjd3y1G/d/uw+10sPshV3k+IR16mwIcGEwPB2YD9wV78Lu/urRYTNbCHSoxNpEqo2dB/IYNW0RH23ax2+G9uaGs08OuySR4xbWr0Wt3X1LMLwVaF1Gu7pmlmVm75vZ0JIzg1NmNwKvx0weZGZLzew1MzutcssWqRpFxc4rSzfzzUnvsHLbfibfGFHISNJK2BGNmc0F2pQy6+7YEXd3M/MyVnOyu28ysy7AP83sI3dfEzN/ErDA3d8OxhcHyxwws8uAl4DuZdQ3BhgDkJmZGfd+iSRSXmERz2dvYvKCNazfdYgurRowc/TZ9M8s9TKmSFJIWNC4++Cy5pnZNjNr6+5bzKwtsL2MdWwKvnPMbD7QD1gTrOOXQCvgtpj2n8cMv2pmk8yspbvvLGXdU4ApAJFIpKygE6kSB/IKmfnBeh57ey3b9+dxevsmPDy8P187rQ3pemeZJLmwrtHMAUYAE4Pvl0s2CO5EO+TueWbWEjgXuC+YdyvwdeBidy+OWaYNsC04ShpI9NTgrkTvjMiJ2n0wn2nvrGX6e+vZd7iAc7q24IFhfTm3WwvMFDBSM4QVNBOB2WY2ClgPDAMwswgw1t1vBXoBk82smGhgTHT3FcHyjwTLvRf8Z3whuMPsGmCcmRUCh4Hr3F1HK1LtbN57mEffzuGZhRs5XFDE105tzbgLu9JPp8ikBjL9HI6eOsvKygq7DEkBa3Yc4JH5a3hpySaKHYb0bce4C7rSXd0sSxIys2x3j5TXTm8GEKkCH+XuY9L81bz+8VZqp6fxnYGZjP5KFzo0qx92aSIJp6ARSRB35701u5g0fw3/Xr2TRnUzGH9hV24+tzMtG9YJuzyRKqOgEalkxcXOm59sY9L8NSzduJeWDetw16U9ueHsTL02RlKSgkakkhQUFTNnyWYe+dcaVm0/QMfm9fjN0N5cM6ADdWulh12eSGgUNCIVdKSgiFmLNjJlQQ6b9h6mZ5tGPHRdXy4/vS0ZeieZiIJG5ETtO1zAk++vZ+q/17LrYD4DTm7Gr4ecxkU9T9IzMCIxFDQix2n7/iNM/fc6nnp/PfvzCrmgRyvGX9iVgZ2bK2BESqGgEYnTxt1omPNRAAALIklEQVSHmLxgDbOzciksKuYbp7dl3AVd6d2+SdiliVRrChqRcqzcup+H56/mlWVbSDfj6gHtGfOVrnRu2SDs0kSSgoJGpAzZ63cz6a01zPt0O/Vrp3PLuZ0YdV4X2jSpG3ZpIklFQSMSw93512c7mDR/DQvX7qZZ/Vp8f3APRpxzMk3r1w67PJGkpKARIdrR2GvLt/Dw/DV8vPlz2japy8+vOJXrB3akfm39NxGpCP0PkpSWV1jEi4s3MXlBDmt3HqRLywbcd/UZDO3XntoZegZGpDIoaCQlHcwr5OmFG3j07Ry2fZ5H7/aNmTS8P19XR2MilU5BIyllz8F8pr27junvrWPvoQIGdWnB77/dh/O6tdQzMCIJoqCRlLBl32EeXbCWpxdu4HBBEZcEHY31V0djIgkXStCYWXNgFtAJWAcMc/c9pbQrAj4KRje4+1XB9GnABcC+YN5Id19i0V9JHwIuAw4F0xcnbk+kusvZcYBH/rWGFz8MOhrr046xF3alhzoaE6kyYR3RTADmuftEM5sQjN9VSrvD7t63jHX82N2fKzHtG0D34HMW8HDwLSlm+aZoR2OvLY92NHb9wExGn9+Fjs3V0ZhIVQsraIYAFwbD04H5lB40J7LeGR7tn/p9M2tqZm3dfUslrFuqOXfn/ZzdTJq/mrdX7aRRnQzGXRDtaKxVI3U0JhKWsIKmdcwP/61A6zLa1TWzLKAQmOjuL8XM+62Z/QKYB0xw9zygPbAxpk1uMO0/gsbMxgBjADIzMyuyLxKy4mJn3qfbmTR/NR9u+KKjseFnZ9JYHY2JhC5hQWNmc4E2pcy6O3bE3d3MvIzVnOzum8ysC/BPM/vI3dcA/000oGoDU4geDf36eOpz9ynBskQikbK2L9VYYVExryzbzMPz1/DZtgN0aFaPe4b25tvqaEykWklY0Lj74LLmmdm2o6e0zKwtsL2MdWwKvnPMbD7QD1gTczSUZ2aPAz8KxjcBHWNW0SGYJjXIkYIins3ayOQFOeTuOcwprRvxh2v7csUZ6mhMpDoK69TZHGAEMDH4frlkAzNrBhxy9zwzawmcC9wXzDsaUgYMBZbHrPcOM3uG6E0A+3R9pub4/MgXHY3tPJBP/8ym/OrKaEdjaXrIUqTaCitoJgKzzWwUsB4YBmBmEWCsu98K9AImm1kxkEb0Gs2KYPmnzKwVYMASYGww/VWitzavJnp7881VtD+SQDv25zH1nbU8+V60o7GvBB2NnaWOxkSSgkVv0EptkUjEs7Kywi5DSti4+xBTFuQwO2sj+UXFXNa7LeMuVEdjItWFmWW7e6S8dnozgFQ7n23bz8Pz1zBn6WbSDK7u34HbLlBHYyLJSkEj1cbiDXuY9NYa5n6yjfq107n5nE7cer46GhNJdgoaCZW78/aqnUyav5r3c3bTtH4tvje4OyMGdaJZA3U0JlITKGgqYOPuQ7yXs4va6WlkpBsZaWnUSjcy0tOolWbUykgjI82oFTP//9qmG7XSosO10tOolZ6WUq+nLyp2Xl++lYf/tZrlmz6nTeO6/OzyXlw/MJMGdfTPUqQm0f/oCliycS8/eW5Zpa3PjBLhEw2nkuNHw+xoiP1fuMW0LzX8ggD8om00DI9uM6PE/FrBNmLH/7OmtC+FZkaaHfNOsPzCYl78MJfJ/8ohZ+dBOrdswL1Xn87Qfu2pk6GHLEVqIgVNBQzu1Zq3f/JVCoudgqJiCoqKKSxyCouLKSjy/xsvKCqOaeMUFhVTUBz9Lixy8kssVxisK7ZNQbFTUFj8RZvg+1B+4RfbKj667Je3GbvuqlAyjGID8fMjhew+mM9p7Rrzl+/059Le6mhMpKZT0FRAvdrpSfU2YHcPwsgpKP4iBEsLyKOBFxtW/xl+xUH4+ZcCNNr+i0CMDdOMNGNov/ac310djYmkCgVNCjGz4BQY1EOnqUSkaujFUCIiklAKGhERSSgFjYiIJJSCRkREEkpBIyIiCaWgERGRhFLQiIhIQiloREQkodTxGWBmO4j29HkiWgI7K7GcZKB9Tg3a59RQkX0+2d1blddIQVNBZpYVTw9zNYn2OTVon1NDVeyzTp2JiEhCKWhERCShFDQVNyXsAkKgfU4N2ufUkPB91jUaERFJKB3RiIhIQilo4mRml5rZSjNbbWYTSplfx8xmBfM/MLNOVV9l5Ypjn39gZivMbJmZzTOzk8OoszKVt88x7a42MzezpL9DKZ59NrNhwd/1x2Y2s6prrGxx/NvONLO3zOzD4N/3ZWHUWVnMbKqZbTez5WXMNzP7Y/DnsczM+ldqAe6uTzkfIB1YA3QBagNLgVNLtBkPPBIMXwfMCrvuKtjnrwL1g+FxqbDPQbtGwALgfSASdt1V8PfcHfgQaBaMnxR23VWwz1OAccHwqcC6sOuu4D5/BegPLC9j/mXAa4ABZwMfVOb2dUQTn4HAanfPcfd84BlgSIk2Q4DpwfBzwMWW3H0Vl7vP7v6Wux8KRt8HOlRxjZUtnr9ngHuAe4EjVVlcgsSzz6OBv7j7HgB3317FNVa2ePbZgcbBcBNgcxXWV+ncfQGw+xhNhgAzPOp9oKmZta2s7Sto4tMe2BgznhtMK7WNuxcC+4AWVVJdYsSzz7FGEf2NKJmVu8/BKYWO7v73qiwsgeL5e+4B9DCzd8zsfTO7tMqqS4x49vlXwA1mlgu8Cny3akoLzfH+fz8uGZW1IkldZnYDEAEuCLuWRDKzNOABYGTIpVS1DKKnzy4ketS6wMxOd/e9oVaVWNcD09z9fjMbBDxhZr3dvTjswpKRjmjiswnoGDPeIZhWahszyyB6uL2rSqpLjHj2GTMbDNwNXOXueVVUW6KUt8+NgN7AfDNbR/Rc9pwkvyEgnr/nXGCOuxe4+1rgM6LBk6zi2edRwGwAd38PqEv0nWA1VVz/30+UgiY+i4DuZtbZzGoTvdg/p0SbOcCIYPga4J8eXGVLUuXus5n1AyYTDZlkP28P5eyzu+9z95bu3sndOxG9LnWVu2eFU26liOff9ktEj2Yws5ZET6XlVGWRlSyefd4AXAxgZr2IBs2OKq2yas0BbgruPjsb2OfuWypr5Tp1Fgd3LzSzO4A3iN6xMtXdPzazXwNZ7j4H+CvRw+vVRC+6XRdexRUX5z7/L9AQeDa472GDu18VWtEVFOc+1yhx7vMbwNfMbAVQBPzY3ZP2aD3Off4h8KiZfZ/ojQEjk/kXRzN7mugvCy2D606/BGoBuPsjRK9DXQasBg4BN1fq9pP4z05ERJKATp2JiEhCKWhERCShFDQiIpJQChoREUkoBY2IiCSUgkYkBGbWqaw36ZbRfqSZtUtkTSKJoqARSQ4jAQWNJCUFjUh4MszsKTP7xMyeM7P6ZjbAzP5lZtlm9oaZtTWza4i+S+4pM1tiZvXM7BdmtsjMlpvZlCR/U7jUcHpgUyQEQcd4a4Hz3P0dM5sKfAJ8Exji7jvM7Frg6+5+i5nNB3509HU3Ztbc3XcHw08As939lRB2RaRcegWNSHg2uvs7wfCTwE+JvrTzzeAAJR0o631TXzWznwD1gebAx4CCRqolBY1IeEqeTtgPfOzug461kJnVBSYR7d1zo5n9iuhLH0WqJV2jEQlPZtDXCcB3iL4NutXRaWZWy8xOC+bvJ9pNAXwRKjvNrCHRt4WLVFsKGpHwrARuN7NPgGbAn4iGxr1mthRYApwTtJ0GPGJmS4A84FFgOdE3EC+q4rpFjotuBhARkYTSEY2IiCSUgkZERBJKQSMiIgmloBERkYRS0IiISEIpaEREJKEUNCIiklAKGhERSaj/D0R3KxGZX5BZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Correlation values for different betas\n",
    "corrs = [-0.5603004848271849, -0.5618695011449486, -0.5253121992485645, -0.45670034315239927, -0.36082410679630594]\n",
    "\n",
    "plt.plot([0, .25, .5, .75, 1], corrs)\n",
    "\n",
    "plt.xlabel('beta')\n",
    "plt.ylabel('corrcoef')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Innvestigate",
   "language": "python",
   "name": "innvestigate"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
